Random-guided search algorithm for complex functions

Optimization is a general goal that has many applications in Engineering, Business, computer science and almost in every operation in life. Devising ways for handling problem optimization is an important yet a challenging task. We look for techniques that are efficient, accurate, and applicable. The search space could have any nature and could have discontinuity or multi-local optima. In this paper, we address this challenge by offering an algorithm that combines the random search techniques with both an effective mapping and a dynamic adjustment of its search behavior. Our proposed algorithm automatically builds two types of triangles over the unity intervals: principal and marginal. These triangles guide the search within both the effective regions of the search domain that most likely contain the optima and the marginal regions of the search domain that less likely contain the optima. Experiments with our prototype implementation showed that our method can effectively find the global optima for rather complicated mathematical functions chosen from well-known benchmarks and perform better than other algorithms.